@Article{JICS-16-016,
author = {Ma , Chenmin},
title = {A Short-Time Traffic Flow Prediction Model Based on TCN-LSTM with Causal Convolutional Layer},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {16},
number = {1},
pages = {016--023},
abstract = {<p style="text-align: justify;">Short term traffic flow prediction is of great significance for traffic management, early guidance
and dispersion, avoiding congestion and improving safety. Traffic flow is affected by multiple factors and
exists strong time dependency. Therefore, it is very meaningful to establish a prediction model with multiple
features, such as time-day-week-biweekly, holiday and weather situation, etc. In this paper, a TCN- LSTM
model with causal convolution block is proposed, which is composed of two subnets: LSTM subnetwork is
used to extract feature from original traffic flow data sequence, three TCN+LSTM subnetworks are used to
extract features from traffic flow data with day-week-biweekly, holiday and weather. TCN is embedded to
maintain causation of the input traffic flow data. Finally, features extracted from the two sub networks are
merged and imported into top-level full connection network. The prediction sequence of the future short-term
traffic flow is obtained at the output layer of FCN. Experimental results show that the proposed TCN-LSTM
model has high accuracy and stability in short-term traffic flow prediction.</p>},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22375.html}
}